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Paper Abstract and Keywords
Presentation 2026-03-06 10:45
Efficient Training and Explainable GNNs for IoT Intrusion Detection via Modified Neighbor Sampling
Rafif Reyhandhia Yusa, Ryo Yamamoto, Satoshi Ohzahata (UEC) NS2025-305
Abstract (in Japanese) (See Japanese page) 
(in English) While the widespread adoption of Internet of Things (IoT) has provided transformative benefits across various sectors, its inherent vulnerabilities continue to pose significant security risks. With threats continuously evolving, traditional rule-based approaches are becoming more difficult to manage and scale. Simultaneously, conventional Machine Learning (ML) approaches struggle to capture the inherent complex relationship between devices in IoT network communication, suffer from performance degradation due to class imbalance in the training data, and remain opaque in their decision making–commonly referred as the "black-box" problem.

To address these challenges, this research proposes an interpretable Network Intrusion Detection System (NIDS) that captures the spatial and topological information of the network by mapping it into a graph structure. By utilizing GraphSAGE for inductive learning, interpretable explanations are generated post-hoc using the Captum library to provide insights into the detection result. Furthermore, the Layer-Neighbor Sampling algorithm and Class-Balanced Loss calculation are introduced to optimize training efficiency and help mitigate the effects of imbalanced data. Experimental results across two benchmark NIDS datasets demonstrate that the proposed framework improves detection rates for minority attack classes and balances Macro F1-scores, significantly reduces training time, and provides insights into the features that drive the model predictions.
Keyword (in Japanese) (See Japanese page) 
(in English) Graph Neural Network / Intrusion Detection / IoT Security / XAI / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 385, NS2025-305, pp. 497-502, March 2026.
Paper # NS2025-305 
Date of Issue 2026-02-25 (NS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NS2025-305

Conference Information
Committee IN NS  
Conference Date 2026-03-04 - 2026-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa-Ken Shichoson Jichi Kaikan 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NS 
Conference Code 2026-03-IN-NS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Efficient Training and Explainable GNNs for IoT Intrusion Detection via Modified Neighbor Sampling 
Sub Title (in English)  
Keyword(1) Graph Neural Network  
Keyword(2) Intrusion Detection  
Keyword(3) IoT Security  
Keyword(4) XAI  
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1st Author's Name Rafif Reyhandhia Yusa  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Ryo Yamamoto  
2nd Author's Affiliation The University of Electro-Communications (UEC)
3rd Author's Name Satoshi Ohzahata  
3rd Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2026-03-06 10:45:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2025-305 
Volume (vol) vol.125 
Number (no) no.385 
Page pp.497-502 
#Pages 6 
Date of Issue 2026-02-25 (NS) 

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